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This book presents recent technologies that explore artificial intelligence (AI) and its scope in Internet of Things (IoT) enabled areas for productivity and the betterment of society. The book aims at targeting audiences of several disciplines to share research, suggest solutions, and future trends in the field of AI using IoT. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both aspects to give a holistic understanding of the AI paradigm for IoT. The book focuses on timely topics related to the field of AI enabled IoT applications at large. The book consists of four major parts: fundamentals, theoretical discussion, critical applications, and the learning algorithms. These contents shall include the basics, types, tools, and techniques of AI. Finally, applications of AI enabled IoT in several areas are presented including health, security, climate change, agricultural engineering, bioinformatics, biomedicine, smart applications, natural language processing, social and economic implications of AI enabled IoT, as well as robotics, sustainability, risk management, seismic data processing, smart grid management, text analysis, security, privacy, and ethics.

This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3  discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data.  Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elementsTo support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained problem. In addition, real-world data is complex and heterogeneous, and often violate the low-level assumptions required by existing compressive sensing techniques. These violations significantly reduce the applicability and effectiveness of existing compressive sensing methods. Third, synchronization of network data reduces the data ranks and increases spatial locality. However, periodic time series exhibit not only misalignment but also different frequencies, which makes it difficult to synchronize data in the time and frequency domains.The primary audience for this book is data engineers, analysts and researchers, who need to deal with big data with missing anomalous and synchronization problems. Advanced level students focused on compressive sensing techniques will also benefit from this book as a reference.

This book discusses the trust models for next-generation Blockchain ecosystems. The book provides a comprehensive discussion on various trust factors involving security, anonymization, reputation, governance, economic models, and other relevant determinants. The book covers various topics in breadth and depth. In addition, it sets the foundation to involve the readers in understanding the core theories supplemented with technical and experimental discussion. The book starts by laying out the foundations of trust models in Blockchain ecosystems. The authors then provide a study of existing trust models Blockchain networks. They then provide identification of trust factors and discuss each trust factor. The book concludes with a future outlook of trust-enabling Blockchain ecosystems.Outlines the trust models for next-generation Blockchain ecosystems;Covers the trust issues in various Blockchain ecosystems running in public, private, consortium, and cloud environments;Features issues such has privacy, security, scalability, and requirements in Blockchain.

Emerging Practices in Telehealth: Best Practices in a Rapidly Changing Field is an introduction to telehealth basics, best practices and implementation methods. The book guides the reader from start to finish through the workflow implementation of telehealth technology, including EMRs, clinical workflows, RPM, billing systems, and patient experience. It also explores how telehealth can increase healthcare access and decrease disparities across the globe. Practicing clinicians, medical fellows, allied healthcare professionals, hospital administrators, and hospital IT professionals will all benefit from this practical guidebook.

Satellite Interferometry Data Interpretation and Exploitation: Case Studies from the European Ground Motion Service (EGMS) focuses on the interpretation and exploitation of data obtained from InSAR, thus enabling millimeter-scale deformation measurements from space. The most emblematic InSAR service, the European Ground Motion Service (EGMS), opens a wide range of new applications. However, for effective use of raw data, interpretation techniques and methods are required. This book presents interpretation protocols that can be applied to any InSAR data, as well as the most relevant technical aspects and boundaries of measurement points. Detailed case studies are reviewed to demonstrate points. This book will be a valuable resource for remote sensing specialists, as well as non-specialists in geotechnics, geology and other geosciences who are looking to apply InSAR data techniques in their research.

There is a need to keep track of advancements related to the network technologies and further investigate several research challenges to overcome for the ease of users. With this goal, the book provides most recent and prominent research works have been done related to the network technologies.

Computational Intelligence in Image and Video Processing presents introduction, state-of-the-art and adaptations of computational intelligence techniques and their usefulness in image and video enhancement, classification, retrieval, forensics and captioning. It covers an amalgamation of such techniques in diverse applications of image and video processing.Features:A systematic overview of state-of-the-art technology in computational intelligence techniques for image and video processingAdvanced evolutionary and nature-inspired approaches to solve optimization problems in the image and video processing domainOutcomes of recent research and some pointers to future advancements in image and video processing and intelligent solutions using computational intelligence techniquesCode snippets of the computational intelligence algorithm/techniques used in image and video processingThis book is primarily aimed at advanced undergraduates, graduates and researchers in computer science and information technology. Engineers and industry professionals will also find this book useful.

This book investigates the most advanced theories and methodologies of array beamforming, with a focus on antenna array enabled wireless communication technology.Combining with the current development needs and trends of wireless communication technology around the world, the authors explore the potentials and challenges of large-scale antenna array beamforming technology in next-generation mobile communication and some important emerging application scenarios. The book first introduces the basic structure of antenna array hierarchical codebook and channel estimation with high dimensionality, with which the time cost of searching the channel information can be effectively reduced. It then explicates high-efficiency beamforming transmission methods for point-to-point transmission, full-duplex point-to-point transmission, and point-to-multipoint transmission where array beamforming enabled non-orthogonal multiple access (NOMA) technologies for typical two-user systems and general multi-user systems are emphasized. The book also discusses array beamforming enabled unmanned aerial vehicle (UAV) communications and array beamforming enabled space/air/ground communications, with the uniqueness and relative solutions for single UAV systems and multi-UAV networks being analyzed.This will be a vital reference for researchers, students, and professionals interested in wireless communications, array beamforming, and millimeter-wave communications.

This book discusses 3D printing of sensors, actuators, and antennas and illustrates how manufacturers can create smart materials that can be effectively used to prepare low-cost products. The book also includes how to select the appropriate process for your manufacturing needs.

This book outlines the recent advancements in the field of artificial intelligence (AI) and addresses how useful it is in achieving truly sustainable solutions. The book also serves as a useful reference literature in developing sustainable engineering solutions to various social and techno-commercial issues of global significance. This book is organized into two sections: section 1 is focused on fundamentals and principles of AI to lay the groundwork for the second section. Section 2 explores the sustainable engineering solutions development using AI, which addresses challenges in various computing techniques and opportunities in engineering design for sustainable development using IoT/AI and smart cities. Applications include waste minimization, re-manufacturing, reuse and recycling technologies using IoT/AI, Industry 4.0, intelligent and smart grid systems, energy conservation using technology, and robotic process automation (RPA). The book is ideal for the engineers, researchers and students interested in how AI can aid in sustainable development applications.

This book presents applications of cognitive management and cognitive computing in the fields of risk management, cognitive fraud detection, and in business decision making. The book provides insights on how cognitive management and cognitive computing enable businesses to quickly augment human intelligence and help humans perform tasks better. For example, the authors describe how by analyzing patterns in big data, small data, and "e;dark data,"e; cognitive technologies can detect human behavior and suggest options for personalizing of products and services. The book studies companies in industries such as automotive, airline, health care, retail, wealth management, and litigation who have adopted these approaches.Presents applications of cognitive computing and cognitive management used in augmenting and empowering business decisions;Shows how to employ the Internet of Things in businesses using a cognitive management framework;Discusses technical aspects and alternatives to traditional tools, algorithms, and methodologies in cognitive computing.